Description

This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.

Compact and Fast Machine Learning Accelerator for IoT Devices

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Hardback by Hantao Huang , Hao Yu

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This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review... Read more

    Publisher: Springer Verlag, Singapore
    Publication Date: 18/12/2018
    ISBN13: 9789811333224, 978-9811333224
    ISBN10: 981133322X

    Number of Pages: 149

    Non Fiction , Computing

    Description

    This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.

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